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dc.contributor.authorKrömer, Pavel
dc.contributor.authorOwais, Suhail Sami Jebour
dc.contributor.authorPlatoš, Jan
dc.contributor.authorSnášel, Václav
dc.date.accessioned2013-08-26T10:42:58Z
dc.date.available2013-08-26T10:42:58Z
dc.date.issued2013
dc.identifier.citationComputers & Mathematics with Applications. 2013, vol. 66, issue 2, p. 190-200.cs
dc.identifier.issn0898-1221
dc.identifier.urihttp://hdl.handle.net/10084/100652
dc.description.abstractThere are various techniques for data mining and data analysis. Among them, hybrid approaches combining two or more fundamental methods gain importance as the complexity and dimension of real world problems and data sets grows. Fuzzy sets and fuzzy logic can be used for efficient data classification by the means of fuzzy rules and classifiers. This study presents an application of genetic programming to the evolution of fuzzy rules based on the concept of extended Boolean queries. Fuzzy rules are used as symbolic classifiers learned from data and used to label data records and to predict the value of an output variable. An example of the application of such a hybrid evolutionary-fuzzy data mining approach to a real world problem is presented.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesComputers & Mathematics with Applicationscs
dc.relation.urihttp://dx.doi.org/10.1016/j.camwa.2013.02.017cs
dc.subjectfuzzy rulescs
dc.subjectgenetic programmingcs
dc.subjectfuzzy information retrievalcs
dc.subjectdata miningcs
dc.subjectapplicationcs
dc.titleTowards new directions of data mining by evolutionary fuzzy rules and symbolic regressioncs
dc.typearticlecs
dc.identifier.doi10.1016/j.camwa.2013.02.017
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume66cs
dc.description.issue2cs
dc.description.lastpage200cs
dc.description.firstpage190cs
dc.identifier.wos000321941300010


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